Gaussian Process Regression Applied to VRLA Battery Voltage Prediction in Photovoltaic Off-Grid Systems

Autores/as

  • Iván Sanz Gorrachategui Group of Power Electronics and Microelectronics (GEPM), Instituto de Investigación en Ingeniería de Aragón (I3A), Universidad de Zaragoza
  • Carlos Bernal Ruiz Group of Power Electronics and Microelectronics (GEPM), Instituto de Investigación en Ingeniería de Aragón (I3A), Universidad de Zaragoza
  • Antonio Bono Nuez Human OpenWare Research Lab (HOWLab), Instituto de Investigación en Ingeniería de Aragón (I3A), Universidad de Zaragoza
  • Milutin Pajovic Mitsubishi Electric Research Laboratory (MERL), Boston
  • Gabriel Martínez Ruata Group of Power Electronics and Microelectronics (GEPM), Instituto de Investigación en Ingeniería de Aragón (I3A), Universidad de Zaragoza

DOI:

https://doi.org/10.26754/jji-i3a.201802826

Resumen

This study addresses the use of GPR techniques for VRLA battery voltage prediction purposes in PV off-grid systems. The goal is to know whether the system is able to endure a predictable power consumption pattern without running out of energy. Two approaches are considered: sample based prediction and pattern-based forecasting.

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Biografía del autor/a

  • Carlos Bernal Ruiz, Group of Power Electronics and Microelectronics (GEPM), Instituto de Investigación en Ingeniería de Aragón (I3A), Universidad de Zaragoza

     

     
  • Antonio Bono Nuez, Human OpenWare Research Lab (HOWLab), Instituto de Investigación en Ingeniería de Aragón (I3A), Universidad de Zaragoza

     

     
  • Milutin Pajovic, Mitsubishi Electric Research Laboratory (MERL), Boston

     

     
  • Gabriel Martínez Ruata, Group of Power Electronics and Microelectronics (GEPM), Instituto de Investigación en Ingeniería de Aragón (I3A), Universidad de Zaragoza

     

     

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Publicado

2018-05-25

Cómo citar

Sanz Gorrachategui, I., Bernal Ruiz, C., Bono Nuez, A., Pajovic, M., & Martínez Ruata, G. (2018). Gaussian Process Regression Applied to VRLA Battery Voltage Prediction in Photovoltaic Off-Grid Systems. Jornada De Jóvenes Investigadores Del I3A, 6. https://doi.org/10.26754/jji-i3a.201802826